The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research

Prevention Science, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

Abstract Beginning with the successful sequencing of human genome two decades ago, possibility developing personalized health interventions based on one’s biology has captured imagination researchers, medical providers, and individuals seeking care services. However, application a medicine approach to emotional behavioral lagged behind development approaches for physical conditions. There is potential value in improved methods integrating biological science prevention identify risk protective mechanisms that have underpinnings, then applying knowledge inform intervention services health. This report represents work task force appointed by Board Society Prevention Research explore challenges recommendations integration sciences. We present state barriers progress approaches, followed recommended strategies would promote responsible Recommendations are grounded Community-Based Participatory goal centering equity future research aimed at disciplines ultimately improve well-being those who disproportionately experienced or experiencing problems.

Language: Английский

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Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility

Neuropsychopharmacology, Journal Year: 2024, Volume and Issue: 50(1), P. 67 - 84

Published: Sept. 6, 2024

Language: Английский

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Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation

Frontiers in Neuroinformatics, Journal Year: 2025, Volume and Issue: 18

Published: Jan. 7, 2025

Neuroimaging is an indispensable tool in neuroscience and medical research, enabling precise investigations into brain structure function (Yen, Lin, Chiang 2023;Yan et al. 2022;Shoeibi 2023;Botvinik-Nezer Wager 2023;Leite 2024;Wager Smith 2003). Techniques such as Magnetic Resonance Imaging (MRI) generate vast amounts of sensitive data, rich insights yet fraught with privacy challenges (Saponaro 2022;Cali 2023;Li 2020;Zou 2024;Acar 2023). As scientific progress depends on data sharing collaboration (Martone 2023), balancing these needs robust preservation has become a critical concern (Zhang 2020). This special issue addresses this challenge by exploring innovative methodologies, frameworks, technologies that advance the field while safeguarding individual privacy.The aims to promote interdisciplinary research privacy-preserving solutions for neuroimaging analysis, ensuring compliance ethical legal standards (Li It seeks balance utility protections fostering methods anonymization, leveraging AI tools federated learning differential privacy, aligning global governance frameworks (Zou 2024;Jeon 2020;Dwork 2006;Abadi 2016). serves roadmap platform dialogue among neuroscientists, researchers, ethicists, policymakers.This features five papers exemplify breadth depth at intersection neuroimaging, artificial intelligence. Each contribution highlights unique facet landscape, collectively offering comprehensive exploration field's current state future potential.The first paper 1 tackles pervasive inflated effect sizes small-sample studies, undermines reproducibility generalizability. By employing hierarchical Bayesian models, authors demonstrate how statistical recalibration can improve reliability findings collaborative metaanalyses across studies. methodological sets foundation shared not only secure but also statistically robust.The second 2 explores AI-driven segmentation intracranial haemorrhage detection CT scans. Leveraging self-supervised weakly-supervised learning, study need label-efficient minimize reliance large, annotated datasets. work showcases innovations enhance efficiency maintain particularly resource-constrained environments where annotation bottleneck.Federated takes centre stage third fourth papers, both which highlight its potential decentralized analysis. The 3 introduces framework Alzheimer's disease detection, incorporating aggregation techniques protect during model training. Similarly, 4 presents Sparse Federated Learning (NeuroSFL), optimizes communication focusing sparse sub-networks. Together, studies underscore adaptability scalability cornerstone privacypreserving research.The final 5 adopts broader lens, examining alignment neuroinformatics practices. identifying gaps existing regulations proposing strategies harmonization, provide integrating within complex landscape governance. emphasizes importance technical advancements principles, trust transparency research.Artificial intelligence driving force behind many contributions issue, powerful privacy. methodologies explainable enable analysis trustworthiness (Yuste 2023;White, Blok, Calhoun 2022;Yang 2022). These address challenges, mitigating risks remains private without compromising utility. particular, emerges transformative approach, allowing researchers train models collaboratively raw data. sparsity-focused presented scale meet demands heterogeneous Complementary blockchain hold promise further enhancing security accountability, though their integration routine workflows challenge.Despite advancements, significant persist. Balancing fundamental tension, often introduce trade-offs performance or 2023;Mitrovska 2024). For instance, are susceptible degradation non-IID (non-independent identically distributed) settings, common scenario neuroimaging. computational may limit accessibility smaller institutions, exacerbating inequities field.Ethical societal add another layer complexity (Aboy, Minssen, Vayena 2024;van Kolfschooten van Oirschot Cognitive informed consent, equitable access benefits ongoing concerns (Bublitz, Molnár-Gábor, Soekadar rapid evolution outpaces development regulatory creating misalignments between technological capabilities oversight (Ratto Trabucco 2023;Ienca Ignatiadis 2020;Wajnerman Paz 2022;Jwa Martinez-Martin 2024;Yuste 2017;Genser, Damianos, Yuste Addressing will require sustained stakeholders, including technologists, policymakers (Ligthart 2023;Bublitz, 2024).This emphasizing advancing maintaining presenting cutting-edge practical real-world applications, offer research. works coexist, disciplines.The ubiquity amplifies dynamic adaptive evolve alongside advancements. intersects innovation, static siloed insufficient overlapping Instead, flexible approaches align AI's essential responsible progress.This testament tackling technology, ethics, neuroscience. it lays groundwork thrives environment trust, transparency, progress. We invite readers engage contributions, conversation shaping more beyond. would have been possible dedication authors, whose forms foundation, reviewers, constructive feedback ensured rigor, communities, drive discovery.

Language: Английский

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Advanced Magnetic Resonance Imaging for Early Diagnosis and Monitoring of Movement Disorders

Brain Sciences, Journal Year: 2025, Volume and Issue: 15(1), P. 79 - 79

Published: Jan. 16, 2025

Advanced magnetic resonance imaging (MRI) techniques are transforming the study of movement disorders by providing valuable insights into disease mechanisms. This narrative review presents a comprehensive overview their applications in this field, offering an updated perspective on potential for early diagnosis, monitoring, and therapeutic evaluation. Emerging MRI modalities such as neuromelanin-sensitive imaging, diffusion-weighted magnetization transfer relaxometry provide sensitive biomarkers that can detect microstructural degeneration, iron deposition, connectivity disruptions key regions like substantia nigra. These enable earlier more accurate differentiation disorders, including Parkinson's disease, progressive supranuclear palsy, multiple system atrophy, corticobasal Lewy body frontotemporal dementia, Huntington's dystonia. Furthermore, provides objective metrics tracking progression assessing efficacy, making it indispensable tool clinical trials. Despite these advances, absence standardized protocols limits integration routine practice. Addressing gap incorporating systematically could bring field closer to leveraging advanced personalized treatment strategies, ultimately improving outcomes individuals with disorders.

Language: Английский

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Bias in data-driven replicability analysis of univariate brain-wide association studies

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 19, 2025

Abstract Recent studies have used big neuroimaging datasets to answer an important question: how many subjects are required for reproducible brain-wide association studies? These data-driven approaches could be considered a framework testing the reproducibility of several models and measures. Here we test part this framework, namely estimates statistical errors univariate brain-behaviour associations obtained from resampling large with replacement. We demonstrate that reported largely consequence bias introduced by random effects when sampling replacement close full sample size. show future meta-analyses can avoid these biases only up 10% discuss implications reproducing mass-univariate requires tens-of-thousands participants, urging researchers adopt other methodological approaches.

Language: Английский

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Predicting Parkinson’s disease trajectory using clinical and functional MRI features: A reproduction and replication study

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317566 - e0317566

Published: Feb. 21, 2025

Parkinson’s disease (PD) is a common neurodegenerative disorder with poorly understood physiopathology and no established biomarkers for the diagnosis of early stages prediction progression. Several neuroimaging have been studied recently, but these are susceptible to several sources variability related instance cohort selection or image analysis. In this context, an evaluation robustness such variations in data processing workflow essential. This study part larger project investigating replicability potential PD. Here, we attempt fully reproduce (reimplementing experiments same methods, including collection from database) replicate (different and/or method) models described (Nguyen et al., 2021) predict individual’s PD current state progression using demographic, clinical features (fALFF ReHo extracted resting-state fMRI). We use Progression Markers Initiative dataset (PPMI, ppmi-info.org), as aim original cohort, imaging machine learning closely possible information available paper code. also investigated methodological selection, feature extraction pipelines sets input features. Different criteria were used evaluate reproduction compare results ones. Notably, obtained significantly better than chance performance analysis pipeline closest that ( R 2 > 0), which consistent its findings. addition, performed partial derived provided by authors study, close The challenges encountered while attempting (fully partially) replicating work likely explained complexity studies, particular settings. provide recommendations further facilitate reproducibility studies future.

Language: Английский

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Testing the sensitivity of diagnosis‐derived patterns in functional brain networks to symptom burden in a Norwegian youth sample

Human Brain Mapping, Journal Year: 2024, Volume and Issue: 45(3)

Published: Feb. 15, 2024

Abstract Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting‐state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples politico‐cultural contexts has not been established. We investigated whether previously cross‐diagnostic case–control autism spectrum disorder (ASD)‐specific pattern of RSFC (discovery sample; aged 5–21 from New York City, USA; n = 1666) could be validated Norwegian convenience‐based youth sample (validation 9–25 Oslo, Norway; 531). As test generalisability, we if these diagnosis‐derived were sensitive levels symptom burden both samples, based on an independent measure burden. Both ASD‐specific across samples. Connectivity significantly associated with thematically appropriate dimensions discovery sample. In validation sample, showed weak, inverse relationship symptoms conduct problems, hyperactivity prosociality, while was linked symptoms. Diagnosis‐derived developmental clinical US convenience youth, however, they health

Language: Английский

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A cortical surface template for human neuroscience

Nature Methods, Journal Year: 2024, Volume and Issue: 21(9), P. 1736 - 1742

Published: July 16, 2024

Neuroimaging data analysis relies on normalization to standard anatomical templates resolve macroanatomical differences across brains. Existing human cortical surface sample locations unevenly because of distortions introduced by inflation the folded cortex into a shape. Here we present onavg template, which affords uniform sampling cortex. We created template based openly available high-quality structural scans 1,031 brains-25 times more than existing templates. optimized vertex anatomy, achieving an even distribution. observed consistently higher multivariate pattern classification accuracies and representational geometry inter-participant correlations other templates, only needs three-quarters as much achieve same performance compared with The also reduces CPU time algorithms 1.3-22.4% due less variation in number vertices each searchlight.

Language: Английский

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Morphologic alterations of the fear circuitry: the role of sex hormones and oral contraceptives

Frontiers in Endocrinology, Journal Year: 2023, Volume and Issue: 14

Published: Nov. 7, 2023

Background Endogenous sex hormones and oral contraceptives (OCs) have been shown to influence key regions implicated in fear processing. While OC use has found impact brain morphology, methodological challenges remain be addressed, such as avoiding selection bias between users non-users, well examining potential lasting effects of intake. Objective We investigated the current use, interplay hormonal milieu history contraception on structural correlates circuitry. also examined role endogenous exogenous within this network. Methods recruited healthy adults aged 23-35 who identified women currently using ( n = 62) or having used 37) solely combined OCs, never any 40), men 41). Salivary users’ salivary ethinyl estradiol (EE) were assessed liquid chromatography – tandem mass spectrometry. Using magnetic resonance imaging, we extracted surface-based gray matter volumes (GMVs) cortical thickness (CT) for interest Exploratory whole-brain analyses conducted with voxel-based morphometry methods. Results Compared men, all three groups exhibited a larger GMV dorsal anterior cingulate cortex, while only showed thinner ventromedial prefrontal cortex. Irrespective menstrual cycle phase, thicker right insular cortex than past users. associations unclear, that EE dosage had greater anatomy compared levels progestin androgenicity, lower doses being associated smaller GMVs. Discussion Our results highlight difference (a fear-promoting region), reduced CT fear-inhibiting region) specific use. Precisely, finding was driven by doses. These findings may represent vulnerabilities anxiety stress-related disorders. little evidence durable anatomical effects, suggesting intake can (reversibly) affect fear-related morphology.

Language: Английский

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Data-driven, connectome-wide analysis identifies psychosis-specific brain correlates of fear and anxiety

Molecular Psychiatry, Journal Year: 2024, Volume and Issue: 29(9), P. 2601 - 2610

Published: March 19, 2024

Language: Английский

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